Reporting & Visualization

Purpose

Present benchmark results and analysis in clear, reproducible format suitable for academic or professional audiences.

Report Structure

1. Executive Summary

• One paragraph: What was tested, key findings
• Example: "Heap-based merge achieves 60M elements/sec on M2, 2.3× faster than Java due to zero-cost abstractions"

2. Experimental Setup

• Hardware: CPU model, clock, cache sizes
• Software: Compiler/JVM version, flags
• Workload: Input sizes, distributions
• Reproducibility: Seed, trial count

3. Results

• Tables with absolute numbers
• Charts comparing implementations
• Statistical significance noted

4. Analysis

• Interpret results (use performance_interpretation skill)
• Explain bottlenecks
• Compare to theoretical predictions

5. Conclusions

• Summary of findings
• Recommendations
• Future work

Table Formats

Performance Comparison

| Language | k | Elements | Throughput | Latency | Memory |
|----------|---|----------|------------|---------|--------|
| Java | 100 | 10M | 45M/sec | 22ns | 1.6KB |
| C++ | 100 | 10M | 60M/sec | 17ns | 1.6KB |
| Rust | 100 | 10M | 62M/sec | 16ns | 1.6KB |

Scaling Analysis

| k | Predicted | Observed | Ratio |
|---|-----------|----------|-------|
| 10 | 50M/sec | 48M/sec | 0.96 |
| 100 | 35M/sec | 37M/sec | 1.06 |
| 1000 | 25M/sec | 23M/sec | 0.92 |

Chart Descriptions (Text-Based)

Throughput vs k

Throughput (M elements/sec)
80 ┤             Linear (k≤8)
70 ┤         ●
60 ┤       ●   ○ C++/Rust Heap
50 ┤     ●       ○
40 ┤   ●           ○ Java Heap
30 ┤ ●               ○
   └─────────────────────────
   10  50  100      1000  k

Latency Distribution

Count
100 ┤     ╭─╮
 80 ┤   ╭─╯ ╰─╮
 60 ┤  ╭╯     ╰─╮
 40 ┤ ╭╯        ╰─╮
 20 ┤╭╯           ╰──
    └───────────────
    10  20  30  40ns

Reproducibility Section

Always include:

## Reproducibility

**Hardware**:
- CPU: Apple M2, 3.5 GHz, 192KB L1, 16MB L2
- RAM: 16GB unified memory

**Software**:
- Java: OpenJDK 17.0.5, JMH 1.35
- C++: Clang 15.0, -O3 -march=native
- Rust: 1.70.0, --release

**Workload**:
- k iterators, 10K elements each
- Elements: uniform random [0, 100K), seed=42
- Trials: 10 warmup + 50 measurement

**Source**: github.com/user/repo/commit/abc123

Key Findings Format

Use bullet points with numbers:

• Rust 38% faster than Java for k=100 (62M vs 45M elements/sec)
• Linear scan optimal for k ≤ 8 (cache-friendly)
• Heap bottleneck: branches (60%) > memory (30%) > CPU (10%)

Cross-Skill Integration

Requires: benchmark_design (data), performance_interpretation (analysis) Feeds into: technical_exposition